Intelligent Underwriting: How AI Agents Improve Risk Models in Real-Time

Assessing financial risk has always been tricky. Underwriters must analyze and understand various components and data points to gauge whether or not an individual/entity will default on a loan or the potential cost of an insurance claim. This is a demanding job – not just from a time perspective but also from analyzing a plethora of information to make predictions. 

When it comes to analyzing extensive datasets, artificial intelligence is becoming a key differentiator. It’s allowing financial institutions to develop better risk models by analyzing a lot more information in a lot less time. An AI-powered bank statement analyzer can analyze financial statements to give you a complete picture of someone’s financial health, cash flow, and transaction anomalies. 

For underwriting, financial institutions can build an agentic workflow of multiple AI agents that allow them to assess risk with much more granularity. It is a new era of risk modeling where a master orchestrator oversees multiple AI agents – from data collection to compliance – operating to perform specific tasks. 

Read More: Understanding Agentic Systems: Workflows vs. Agents

Pinning AI-Driven Risk Modeling Against Traditional Risk Modeling ‍

Traditional underwriting is largely governed by criteria set by the institution, historical records, and underwriters’ judgments. AI, on the other hand, can process traditional (financial statements, medical history, etc.) and non-traditional data points such as social media and online behaviors. 

In the image above, we’ve positioned traditional against an AI-powered underwriting workflow. The traditional approach is a linear, human-led process that is slower, subjective, and often relies on historical data and static rules. 

The AI-powered process, however, replaces the static, manual, and slow decision-making model with a real-time, automated, and insight-rich system — delivering faster, more accurate, and scalable results. The data lake, where all the data is stored, helps make the model adaptable and learn on the job.  ‍

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Why Does it Matter for Financial Institutions? 

AI-powered credit underwriting has multiple benefits for financial institutions, from eliminating manual errors to accommodating a large number of data points for analysis. Let us list why it matters for financial institutions: 

Better Accuracy & Efficiency for Assessing Risks 

AI systems can discern patterns and correlations in the cash flow, transactions, online behavior, etc., on a much larger scale and a shorter time window. That’s how AI-based risk models paint a more granular picture of applicant risk in real-time. 

The shortened decisioning period ensures that there are no drop-offs in the onboarding. The current underwriting process is also a bottleneck for the customers. A Capgemini report discovered that 42% of policyholders find the current underwriting process complex and lengthy. 

For the banks, better accuracy means more precise differentiation between low-risk and high-risk customers. The direct benefit of this differentiation is the improvement in the bottom line as the credit losses are significantly reduced. 

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Strengthening Regulatory Compliance and Transparency

AI models are being designed with transparency and auditability in mind. Regulations like the U.S. Equal Credit Opportunity Act, which requires lenders to provide specific adverse action reasons. If the models can provide the reasons for making a particular decision, potential risks are dispelled. 

These models don’t act as opaque black boxes where the reason for a decision is not explained. This approach allows AI-driven decisions to be audited just like traditional ones, satisfying examiners that decisions are based on legitimate risk factors. 

Moreover, AI can eliminate human inconsistencies and bias in underwriting. Properly trained AI models apply the same criteria across all applicants, which can reduce disparate treatment risk (assuming the models are trained on unbiased data). 

This can only be possible if there are no biases in the training data. It’s probably why many banks still employ a “human-in-the-loop” governance model, where AI provides a recommendation and a human underwriter reviews it.  This hybrid approach leverages AI’s efficiency while keeping ultimate accountability with the institution’s staff, an approach regulators encourage.  ‍

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Real-Time Risk Monitoring and Adaptability 

Traditional underwriting often takes a snapshot view of risk at one point in time (e.g., loan origination or policy issuance). AI agents enable a more fluid approach where risk models evolve with changing conditions. This has significant business benefits in both managing portfolio health and seizing market opportunities promptly. 

In lending, real-time AI systems now monitor borrowers’ financial behavior and broader economic signals throughout the life of a loan. This continuous surveillance means that early warning flags are raised if a borrower’s risk profile starts to deteriorate. 

For example, an AI might detect that a small business’s cash flows have been trending down or that the borrower’s industry is facing new headwinds, and immediately alert risk managers. Continuous AI monitoring can thus trigger proactive interventions – such as offering a refinance, adjusting credit terms, or providing advisory support – before a loan goes delinquent.  ‍

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Credit Underwriting with Arya AI 

Arya AI brings a production-ready, agentic underwriting stack that plugs into your existing LOS/LMS and core banking systems to deliver faster, traceable, and regulator-friendly decisions. It combines three pillars:

1. Apex (AI APIs): Pre-trained extractors for bank statements, payslips, KYC, ID documents, signatures, and proof-of-address; PII redaction; sanctions/PEP screening helpers; device & image liveness/fraud signals.
2. Prism (Specialized Models): Tailorable risk models (e.g., affordability, early-warning) with feature stores, reason codes, and out-of-distribution safeguards.
3. Weave (Agent Orchestration): Policy-aware AI agents that coordinate data collection, verification, scoring, pricing, and compliance—with human-in-the-loop (HITL) queues and full audit trails.

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Conclusion

AI stands to completely transform underwriting with incredible speed and adaptability to changes. What used to be a static process that would provide underwriters with a snapshot view of the risks is going to be dynamic. 

The business benefits are clear: higher accuracy in risk selection (translating to fewer losses), lightning-fast underwriting decisions (leading to greater efficiency, deal conversion, and customer satisfaction), stronger compliance controls (reducing legal and reputational risks), and enhanced decision support for staff (yielding better overall outcomes). 

The institutions that embrace AI-driven underwriting in a thoughtful, strategic way will likely enjoy improved portfolio performance, operational excellence, and a stronger competitive position in the market. Connect with us if you’d like to supercharge your underwriting process with AI.